Revision 9fbfbef539cfc60884f6828c62f0aa328335a0b1 authored by Toni Giorgino on 08 January 2008, 00:00:00 UTC, committed by Gabor Csardi on 08 January 2008, 00:00:00 UTC
1 parent da02fed
globalCostMatrix.R
``````###############################################################
#                                                             #
#   (c) Toni Giorgino <toni.giorgino@gmail.com>               #
#       Laboratory for Biomedical Informatics                 #
#       University of Pavia - Italy                           #
#       www.labmedinfo.org                                    #
#                                                             #
#   \$Id: globalCostMatrix.R 83 2008-01-04 00:25:00Z tonig \$
#                                                             #
###############################################################

########################################
## Compute the cost matrix from a local distance matrix

## Wrapper to the native function

`globalCostMatrix` <-
function(lm,
step.matrix=symmetric1,
window.function=noWindow,
native=TRUE,
...) {

## sanity check - be extra cautions w/ binary
if (!is.stepPattern(step.matrix))
stop("step.matrix is no stepMatrix object");

# i = 1 .. n in query sequence, on first index, ie rows
# j = 1 .. m on template sequence, on second index, ie columns
#   Note:  template is usually drawn vertically, up-wise

n <- nrow(lm);
m <- ncol(lm);

# number of individual steps (counting all patterns)
nsteps<-dim(step.matrix)[1];

# clear the cost and step matrix
# these will be the outputs of the binary
cm <- matrix(NA,nrow=n,ncol=m);
sm <- matrix(NA,nrow=n,ncol=m);

# initializer
cm[1,1] <- lm[1,1];

## precompute windowing
wm <- matrix(FALSE,nrow=n,ncol=m);
wm[window.function(row(wm),col(wm),
query.size=n, template.size=m,
...)]<-TRUE;

## this call could be optimized
tmp<-.C("computeCM",NAOK=TRUE,PACKAGE="dtw",
as.integer(dim(cm)),               # s
as.logical(wm),                    #
as.double(lm),
as.integer(nsteps),
as.double(step.matrix),
cmo=as.double(cm),                     # OUT
smo=as.integer(sm));                   # OUT

cm<-matrix(tmp\$cmo,nrow=n,ncol=m);
sm<-matrix(tmp\$smo,nrow=n,ncol=m);

} else {

####################
## INTERPRETED PURE-R IMPLEMENTATION

warning("Native dtw implementation not available: using (slow) interpreted fallback");
# now walk through the matrix, column-wise and row-wise,
# and recursively compute the accumulated distance. Unreachable
# elements are handled via NAs (removed)
dir <- step.matrix;
npats <- attr(dir,"npat");
for (j in 1:m) {
for (i in 1:n) {
## It is ok to window on the arrival point (?)
if(!window.function(i,j, query.size=n, template.size=m, ...)) { next; }

clist<-numeric(npats)+NA;
for (s in 1:nsteps) {
## current pattern
p<-dir[s,1];
## ii,jj is the cell from where potentially we could
## have come from.
ii<-i-dir[s,2];                 # previous step in inp
jj<-j-dir[s,3];                 # previous step in tpl
if(ii>=1 && jj>=1) {            # element exists?
cc<-  dir[s,4];               # step penalty
if(cc == -1) {		#  -1? cumulative cost:
clist[p]<-cm[ii,jj];	#  there must be exactly 1 per pattern
} else {			#  a cost for
clist[p]<-clist[p]+cc*lm[ii,jj];
}
}
}

## no NAs in clist at this point BUT clist can be empty
## store in cost matrix
minc<-which.min(clist);           # pick the least cost
if(length(minc)>0) {          	# false if clist has all NAs
cm[i,j]<-clist[minc];
sm[i,j]<-minc;			# remember the pattern picked
}
}
}
}

## END PURE-R IMPLEMENTATION
####################

out<-list();
out\$costMatrix<-cm;                   # to get distance
out\$directionMatrix<-sm;              # to backtrack
out\$stepPatterns<-step.matrix;        # to backtrack

return(out);
}

``````

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